Odometry feature matching

ABSTRACT

Methods and systems for determining features of interest for following within various frames of data received from multiple sensors of a device are disclosed. An example method may include receiving data from a plurality of sensors of a device. The method may also include determining, based on the data, motion data that is indicative of a movement of the device in an environment. The method may also include as the device moves in the environment, receiving image data from a camera of the device. The method may additionally include selecting, based at least in part on the motion data, features in the image data for feature-following. The method may further include estimating one or more of a position of the device or a velocity of the device in the environment as supported by the data from the plurality of sensors and feature-following of the selected features in the images.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

Sensor fusion includes combining sensor data or data derived fromsensory data from independent sources such that resulting information ismore complete. Data sources for a fusion process may include multipledistinct sensors. Each sensor may provide different information aboutthe same object in an environment, or about the same location in anenvironment, for example. By combining the sensor data, a more completedepiction of the object or location can be provided. As an example, onesensor may include a camera to capture an image of an object, andanother sensor may include location detection capabilities to determinea location of a device used to capture the image. By combining thesensor data, specific location information for the image data and deviceis provided.

SUMMARY

In one example, a method is provided that includes receiving, using aprocessor, data from a plurality of sensors of a device. The method alsoincludes determining, based on the data, motion data that is indicativeof a movement of the device in an environment. The method additionallyincludes as the device moves in the environment, receiving image datafrom a camera of the device. The method further includes selecting,based at least in part on the motion data, features in the image datafor feature-following. The method further includes estimating one ormore of a position of the device or a velocity of the device in theenvironment as supported by the data from the plurality of sensors andby feature-following of the selected features in the images.

In another example, a computer readable memory is provided that isconfigured to store instructions that, when executed by a device, causethe device to perform functions. The functions include receiving, at thedevice, data from a plurality of sensors of the device. The functionsalso include determining, based on the data, motion data that isindicative of a movement of the device in an environment. The functionsadditionally include as the device moves in the environment, receivingimage data from a camera of the device. The functions further includeselecting, based at least in part on the motion data, features in theimage data for feature-following. The functions yet further includeestimating one or more of a position of the device or a velocity of thedevice in the environment as supported by the data from the plurality ofsensors and by feature-following of the selected features in the images.

In another example, a device is provided that comprises one or moreprocessors, and data storage configured to store instructions that, whenexecuted by the one or more processors, cause the device to performfunctions. The functions include receiving, at the device, data from aplurality of sensors of the device. The functions also includedetermining, based on the data, motion data that is indicative of amovement of the device in an environment. The functions additionallyinclude as the device moves in the environment, receiving image datafrom a camera of the device. The functions further include selecting,based at least in part on the motion data, features in the image datafor feature-following. The functions yet further include estimating oneor more of a position of the device or a velocity of the device in theenvironment as supported by the data from the plurality of sensors andby feature-following of the selected features in the images.

These as well as other aspects, advantages, and alternatives, willbecome apparent to those of ordinary skill in the art by reading thefollowing detailed description, with reference where appropriate to theaccompanying figures.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates an example computing device.

FIG. 2 illustrates another example computing device.

FIGS. 3A-3B are conceptual illustrations of a computing device that showa configuration of some sensors of the computing device in FIG. 2.

FIG. 4 is a block diagram of an example method for determining featuresof interest for following within image data, in accordance with at leastsome embodiments described herein.

FIG. 5A -5D are conceptual illustrations of examples for determiningfeatures for following, in accordance with at least some embodimentsdescribed herein.

FIG. 6 is a conceptual illustration of example estimations of motion ofthe device based on camera images and IMU data, in accordance with atleast some embodiments described herein.

DETAILED DESCRIPTION

The following detailed description describes various features andfunctions of the disclosed systems and methods with reference to theaccompanying figures. In the figures, similar symbols identify similarcomponents, unless context dictates otherwise. The illustrative systemand method embodiments described herein are not meant to be limiting. Itmay be readily understood that certain aspects of the disclosed systemsand methods can be arranged and combined in a wide variety of differentconfigurations, all of which are contemplated herein.

Within examples, methods and systems for extracting features of interestfrom raw imagery received by a device that may be moving and operatingin an environment (e.g., received from a camera of the device) that mayallow for following of the features of interest within the raw imageryover time are provided. The features of interest may represent whole orportions of objects, corners of the image data, or points of interest inan environment defined by the image data, and may be observed over thevarious data frames. Based on the features of interest, a pose of thedevice may be updated. For example, by following a given feature ofinterest, an estimation of the position and the motion of the device maybe made.

Example methods may be performed by a device having an applicationprocessor configured to function based on an operating system and aco-processor configured to receive data from a plurality of sensors ofthe device. An example method includes receiving, using a processor,data from a plurality of sensors of a device. The method also includesdetermining, based on the data, motion data that is indicative of amovement of the device in an environment. The method also includes asthe device moves in the environment, receiving image data from a cameraof the device. The method also includes selecting, based at least inpart on the motion data, features in the image data forfeature-following. The method also includes estimating one or more of aposition of the device or a velocity of the device in the environment assupported by the data from the plurality of sensors andfeature-following of the selected features in the images.

Referring now to the figures, FIG. 1 illustrates an example computingdevice 100. In some examples, components illustrated in FIG. 1 may bedistributed across multiple computing devices. However, for the sake ofexample, the components are shown and described as part of one examplecomputing device 100. The computing device 100 may be or include amobile device (such as a mobile phone), desktop computer, laptopcomputer, email/messaging device, tablet computer, or similar devicethat may be configured to perform the functions described herein.Generally, the computing device 100 may be any type of computing deviceor transmitter that is configured to transmit data or receive data inaccordance with methods and functions described herein.

The computing device 100 may include an interface 102, a wirelesscommunication component 104, a cellular radio communication component106, a global position system (GPS) receiver 108, sensor(s) 110, datastorage 112, and processor(s) 114. Components illustrated in FIG. 1 maybe linked together by a communication link 116. The computing device 100may also include hardware to enable communication within the computingdevice 100 and between the computing device 100 and other computingdevices (not shown), such as a server entity. The hardware may includetransmitters, receivers, and antennas, for example.

The interface 102 may be configured to allow the computing device 100 tocommunicate with other computing devices (not shown), such as a server.Thus, the interface 102 may be configured to receive input data from oneor more computing devices, and may also be configured to send outputdata to the one or more computing devices. The interface 102 may beconfigured to function according to a wired or wireless communicationprotocol. In some examples, the interface 102 may include buttons, akeyboard, a touchscreen, speaker(s) 118, microphone(s) 120, and/or anyother elements for receiving inputs, as well as one or more displays,and/or any other elements for communicating outputs.

The wireless communication component 104 may be a communicationinterface that is configured to facilitate wireless data communicationfor the computing device 100 according to one or more wirelesscommunication standards. For example, the wireless communicationcomponent 104 may include a Wi-Fi communication component that isconfigured to facilitate wireless data communication according to one ormore IEEE 802.11 standards. As another example, the wirelesscommunication component 104 may include a Bluetooth communicationcomponent that is configured to facilitate wireless data communicationaccording to one or more Bluetooth standards. Other examples are alsopossible.

The cellular radio communication component 106 may be a communicationinterface that is configured to facilitate wireless communication (voiceand/or data) with a cellular wireless base station to provide mobileconnectivity to a network. The cellular radio communication component106 may be configured to connect to a base station of a cell in whichthe computing device 100 is located, for example.

The GPS receiver 108 may be configured to estimate a location of thecomputing device 100 by precisely timing signals sent by GPS satellites.

The sensor(s) 110 may include one or more sensors, or may represent oneor more sensors included within the computing device 100. Examplesensors include an accelerometer, gyroscope, pedometer, light sensors,microphone, camera(s), infrared flash, barometer, magnetometer, GPS,WiFi, near field communication (NFC), Bluetooth, projector, depthsensor, temperature sensors, or other location and/or context-awaresensors.

The data storage 112 may store program logic 122 that can be accessedand executed by the processor(s) 114. The data storage 112 may alsostore data collected by the sensor(s) 110, or data collected by any ofthe wireless communication component 104, the cellular radiocommunication component 106, and the GPS receiver 108.

The processor(s) 114 may be configured to receive data collected by anyof sensor(s) 110 and perform any number of functions based on the data.As an example, the processor(s) 114 may be configured to determine oneor more geographical location estimates of the computing device 100using one or more location-determination components, such as thewireless communication component 104, the cellular radio communicationcomponent 106, or the GPS receiver 108. The processor(s) 114 may use alocation-determination algorithm to determine a location of thecomputing device 100 based on a presence and/or location of one or moreknown wireless access points within a wireless range of the computingdevice 100. In one example, the wireless location component 104 maydetermine the identity of one or more wireless access points (e.g., aMAC address) and measure an intensity of signals received (e.g.,received signal strength indication) from each of the one or morewireless access points. The received signal strength indication (RSSI)from each unique wireless access point may be used to determine adistance from each wireless access point. The distances may then becompared to a database that stores information regarding where eachunique wireless access point is located. Based on the distance from eachwireless access point, and the known location of each of the wirelessaccess points, a location estimate of the computing device 100 may bedetermined.

In another instance, the processor(s) 114 may use alocation-determination algorithm to determine a location of thecomputing device 100 based on nearby cellular base stations. Forexample, the cellular radio communication component 106 may beconfigured to identify a cell from which the computing device 100 isreceiving, or last received, signal from a cellular network. Thecellular radio communication component 106 may also be configured tomeasure a round trip time (RTT) to a base station providing the signal,and combine this information with the identified cell to determine alocation estimate. In another example, the cellular communicationcomponent 106 may be configured to use observed time difference ofarrival (OTDOA) from three or more base stations to estimate thelocation of the computing device 100.

In some implementations, the computing device 100 may include a deviceplatform (not shown), which may be configured as a multi-layered Linuxplatform. The device platform may include different applications and anapplication framework, as well as various kernels, libraries, andruntime entities. In other examples, other formats or operating systemsmay operate the computing g device 100 as well.

The communication link 116 is illustrated as a wired connection;however, wireless connections may also be used. For example, thecommunication link 116 may be a wired serial bus such as a universalserial bus or a parallel bus, or a wireless connection using, e.g.,short-range wireless radio technology, or communication protocolsdescribed in IEEE 802.11 (including any IEEE 802.11 revisions), amongother possibilities.

The computing device 100 may include more or fewer components. Further,example methods described herein may be performed individually bycomponents of the computing device 100, or in combination by one or allof the components of the computing device 100.

FIG. 2 illustrates another example computing device 200. The computingdevice 200 in FIG. 2 may be representative of a portion of the computingdevice 100 shown in FIG. 1. In FIG. 2, the computing device 200 is shownto include a number of sensors such as an inertial measurement unit(IMU) 202 including a gyroscope 204 and an accelerometer 206, a globalshutter (GS) camera 208, a rolling shutter (RS) camera 210, a frontfacing camera 212, an infrared (IR) flash 214, a barometer 216, amagnetometer 218, a GPS receiver 220, a WiFi/NFC/Bluetooth sensor 222, aprojector 224, a depth sensor 226, and a temperature sensor 228, each ofwhich outputs to a co-processor 230. The co-processor 230 receives inputfrom and outputs to an application processor 232. The computing device200 may further include a second IMU 234 that outputs directly to theapplication processor 232.

The IMU 202 may be configured to determine a velocity, orientation, andgravitational forces of the computing device 200 based on outputs of thegyroscope 204 and the accelerometer 206.

The GS camera 208 may be configured on the computing device 200 to be arear facing camera, so as to face away from a front of the computingdevice 200. The GS camera 208 may be configured to read outputs of allpixels of the camera 208 simultaneously. The GS camera 208 may beconfigured to have about a 120-170 degree field of view, such as a fisheye sensor, for wide-angle viewing.

The RS camera 210 may be configured to read outputs of pixels from a topof the pixel display to a bottom of the pixel display. As one example,the RS camera 210 may be a red/green/blue (RGB) infrared (IR) 4megapixel image sensor, although other sensors are possible as well. TheRS camera 210 may have a fast exposure so as to operate with a minimumreadout time of about 5.5 ms, for example. Like the GS camera 208, theRS camera 210 may be a rear facing camera.

The camera 212 may be an additional camera in the computing device 200that is configured as a front facing camera, or in a direction facingopposite of the GS camera 208 and the RS camera 210. The camera 212 maybe configured to capture images of a first viewpoint of the computingdevice 200 and the GS camera 208 and the RS camera 210 may be configuredto capture images of a second viewpoint of the device that is oppositethe first viewpoint. The camera 212 may be a wide angle camera, and mayhave about a 120-170 degree field of view for wide angle viewing, forexample.

The IR flash 214 may provide a light source for the computing device200, and may be configured to output light in a direction toward a rearof the computing device 200 so as to provide light for the GS camera 208and RS camera 210, for example. In some examples, the IR flash 214 maybe configured to flash at a low duty cycle, such as 5 Hz, or in anon-continuous manner as directed by the co-processor 230 or applicationprocessor 232. The IR flash 214 may include an LED light sourceconfigured for use in mobile devices, for example.

FIGS. 3A-3B are conceptual illustrations of a computing device 300 thatshow a configuration of some sensors of the computing device 200 in FIG.2. In FIGS. 3A-3B, the computing device 300 is shown as a mobile phone.The computing device 300 may be similar to either of computing device100 in FIG. 1 or computing device 200 in FIG. 2. FIG. 3A illustrates afront of the computing device 300 in which a display 302 is provided,along with a front facing camera 304, and a P/L sensor opening 306(e.g., a proximity or light sensor). The front facing camera 304 may bethe camera 212 as described in FIG. 2.

FIG. 3B illustrates a back 308 of the computing device 300 in which arear camera 310 and another rear camera 314 are provided. The rearcamera 310 may be the RS camera 210 and the rear camera 312 may be theGS camera 208, as described in the computing device 200 in FIG. 2. Theback 308 of the computing device 300 also includes an IR-flash 314,which may be the IR flash 214 or the projector 224 as described in thecomputing device 200 in FIG. 2. In one example, the IR flash 214 and theprojector 224 may be one in the same. For instance, a single IR flashmay be used to perform the functions of the IR flash 214 and theprojector 224. In another example, the computing device 300 may includea second flash (e.g., an LED flash) located near the rear camera 310(not shown). A configuration and placement of the sensors may be helpfulto provide desired functionality of the computing device 300, forexample, however other configurations are possible as well.

Referring back to FIG. 2, the barometer 216 may include a pressuresensor, and may be configured to determine air pressures and altitudechanges.

The magnetometer 218 may be configured to provide roll, yaw, and pitchmeasurements of the computing device 200, and can be configured tooperate as an internal compass, for example. In some examples, themagnetometer 218 may be a component of the IMU 202 (not shown).

The GPS receiver 220 may be similar to the GPS receiver 108 described inthe computing device 100 of FIG. 1. In further examples, the GPS 220 mayalso output timing signals as received from GPS satellites or othernetwork entities. Such timing signals may be used to synchronizecollected data from sensors across multiple devices that include thesame satellite timestamps.

The WiFi/NFC/Bluetooth sensor 222 may include wireless communicationcomponents configured to operate according to WiFi and Bluetoothstandards, as discussed above with the computing device 100 of FIG. 1,and according to NFC standards to establish wireless communication withanother device via contact or coming into close proximity with the otherdevice.

The projector 224 may be or include a structured light projector thathas a laser with a pattern generator to produce a dot pattern in anenvironment. The projector 224 may be configured to operate inconjunction with the RS camera 210 to recover information regardingdepth of objects in the environment, such as three-dimensional (3D)characteristics of the objects. For example, the separate depth sensor226 may be configured to capture video data of the dot pattern in 3Dunder ambient light conditions to sense a range of objects in theenvironment. The projector 224 and/or depth sensor 226 may be configuredto determine shapes of objects based on the projected dot pattern. Byway of example, the depth sensor 226 may be configured to cause theprojector 224 to produce a dot pattern and cause the RS camera 210 tocapture an image of the dot pattern. The depth sensor 226 may thenprocess the image of the dot pattern, use various algorithms totriangulate and extract 3D data, and output a depth image to theco-processor 230.

The temperature sensor 228 may be configured to measure a temperature ortemperature gradient, such as a change in temperature, for example, ofan ambient environment of the computing device 200.

The co-processor 230 may be configured to control all sensors on thecomputing device 200. In examples, the co-processor 230 may controlexposure times of any of cameras 208, 210, and 212 to match the IR flash214, control the projector 224 pulse sync, duration, and intensity, andin general, control data capture or collection times of the sensors. Theco-processor 230 may also be configured to process data from any of thesensors into an appropriate format for the application processor 232. Insome examples, the co-processor 230 merges all data from any of thesensors that corresponds to a same timestamp or data collection time (ortime period) into a single data structure to be provided to theapplication processor 232.

The application processor 232 may be configured to control otherfunctionality of the computing device 200, such as to control thecomputing device 200 to operate according to an operating system or anynumber of software applications stored on the computing device 200. Theapplication processor 232 may use the data collected by the sensors andreceived from the co-processor to perform any number of types offunctionality. The application processor 232 may receive outputs of theco-processor 230, and in some examples, the application processor 232may receive raw data outputs from other sensors as well, including theGS camera 208 and the RS camera 210.

The second IMU 234 may output collected data directly to the applicationprocessor 232, which may be received by the application processor 232and used to trigger other sensors to begin collecting data. As anexample, outputs of the second IMU 234 may be indicative of motion ofthe computing device 200, and when the computing device 200 is inmotion, it may be desired to collect image data, GPS data, etc. Thus,the application processor 232 can trigger other sensors throughcommunication signaling on common buses to collect data at the times atwhich the outputs of the IMU 234 indicate motion.

The computing device 200 shown in FIG. 2 may include a number ofcommunication buses between each of the sensors and processors. Forexample, the co-processor 230 may communicate with each of the IMU 202,the GS camera 208, and the RS camera 212 over an inter-integratedcircuit (I2C) bus that includes a multi-master serial single-ended busfor communication. The co-processor 230 may receive raw data collected,measured, or detected by each of the IMU 202, the GS camera 208, and theRS camera 212 over the same I2C bus or a separate communication bus. Theco-processor 230 may communicate with the application processor 232 overa number of communication buses including a serial peripheral interface(SPI) bus that includes a synchronous serial data link that may operatein full duplex mode, the I2C bus, and a mobile industry processorinterface (MIPI) that includes a serial interface configured forcommunicating camera or pixel information. Use of various buses may bedetermined based on need of speed of communication of data as well asbandwidth provided by the respective communication bus, for example.

Within examples herein, the computing device 200 may collect data as thecomputing device 200 moves through an environment, and may be configuredto perform odometry functions. Odometry includes use of data fromsensors that are moving to estimate a change in position over time.Odometry can be used to estimate a position of the computing device 200relative to a starting location so as to determine a trajectory orpathway of the computing device 200. In some examples, a sliding windowof sensor data can be processed as the device moves through theenvironment to determine a path traversed by the computing device 200.

FIG. 4 is a block diagram of an example method for determining featuresof interest for following within image data, in accordance with at leastsome embodiments described herein. Method 400 shown in FIG. 4 presentsan embodiment of a method that, for example, could be used with thecomputing device 100 in FIG. 1, the computing device 200 in FIG. 2, orthe computing device 300 in FIG. 3, for example, or may be performed bya combination of any components of the computing device 100 in FIG. 1,the computing device 200 in FIG. 2, or the computing device 300 in FIG.3. Method 400 may include one or more operations, functions, or actionsas illustrated by one or more of blocks 402-410. Although the blocks areillustrated in a sequential order, these blocks may in some instances beperformed in parallel, and/or in a different order than those describedherein. Also, the various blocks may be combined into fewer blocks,divided into additional blocks, and/or removed based upon the desiredimplementation.

In addition, for the method 400 and other processes and methodsdisclosed herein, the flowchart shows functionality and operation of onepossible implementation of present embodiments. In this regard, eachblock may represent a module, a segment, or a portion of program code,which includes one or more instructions executable by a processor forimplementing specific logical functions or steps in the process. Theprogram code may be stored on any type of computer readable medium, forexample, such as a storage device including a disk or hard drive. Thecomputer readable medium may include a non-transitory computer readablemedium, for example, such as computer-readable media that stores datafor short periods of time like register memory, processor cache andRandom Access Memory (RAM). The computer readable medium may alsoinclude other non-transitory media, such as secondary or persistent longterm storage, like read only memory (ROM), optical or magnetic disks,compact-disc read only memory (CD-ROM), for example. The computerreadable media may also be any other volatile or non-volatile storagesystems. The computer readable medium may be considered a computerreadable storage medium, a tangible storage device, or other article ofmanufacture, for example. The program code (or data for the code) mayalso be stored or provided on other media including communication media,such as a wireless communication media, for example.

In addition, for the method 400 and other processes and methodsdisclosed herein, each block in FIG. 4 may represent circuitry that iswired to perform the specific logical functions in the process.

Functions of the method 400 may be fully performed by a computingdevice, or may be distributed across multiple computing devices and/or aserver. In some examples, the computing device may receive informationfrom sensors of the computing device, or where the computing device is aserver the information can be received from another device that collectsthe information. The computing device could further communicate with aserver to receive information from sensors of other devices, forexample. The method 400 may further be performed by a device that has anapplication processor configured to function based on an operatingsystem and a co-processor configured to receive data from a plurality ofsensors of the device. The sensors may include any sensors as describedabove in any of FIG. 1, FIG. 2, or FIGS. 3A-3B, for example, includingan IMU, a global shutter camera, a rolling shutter camera, a structuredlight projector, a depth camera, an infrared flash, a barometer, amagnetometer, and a temperature sensor.

At block 402, method 400 includes receiving data from a plurality ofsensors of a device. A processor of the device may receive the data viaa number of communication buses within the device. As previously noted,the device may be the computing device 100 in FIG. 1, the computingdevice 200 in FIG. 2, or the computing device 300 in FIG. 3, forexample.

The data may be representative of an environment. The environment may beany environment in which the device may be operating in, any environmentin which the device is operating in proximity to, or any environment ofwhich the device is capturing data of such as environment 500 of FIG.5A. As shown in FIG. 5A, the device may be capturing or sensing anenvironment that may be indicative of the outside of a home. Theenvironment 500 may include a house 504, a tree 516, and a yard 506, forexample. The house may include a window 508, a door 510, a roof 512, andchimney 514.

The sensor data 502 may include any data indicative of environment 500and may be received from any of the sensors described above withreference to FIGS. 1-3. Although not shown in FIG. 5A, the sensor data502 may for example include data that may define depth, color, lighting,temperature, etc. of environment 500. To obtain the data 502, the devicemay be continuously scanning environment 500 as the device operates. Thedevice may utilize a large number of scans of the environment orperiodic scans of the environment using various sensors when obtainingthe sensor data. Successive scans may be multiple scans by varioussensors occurring over time and may be continuous or may occur inintervals.

The sensor data 502 may encompass various types of data and datastructures, may be of various file formats, and may be stored to variousmediums, whether those types of data, file formats, and mediums areknown or have yet to be developed. For example, the sensor data 502 mayinclude one or more digital photographs or one or more digital graphicsthat may represent environment 500, as is shown in FIG. 5A. In otherexamples, the sensor data may include data received from an inertialmeasurement unit (IMU) of the device. The data received from the IMU mayinclude data indicative of a position of the device as it receives thedata indicative of environment 500. This data may be updated as thedevice moves and operates to receive data. For example, the data mayindicate a position of the device using three degrees of freedom (e.g.,along x axis, y axis, and z axis) and an orientation of the device usingthree degrees of freedom (e.g., roll, pitch, yaw). The data may becollected by the IMU over any given time period, and in some examples,the IMU may continuously collect data.

At block 404, the method 400 includes determining based on the data,motion data that is indicative of a movement of the device in theenvironment. To determine the motion data, a processor of the device mayprocess the received sensor data and mine the desired information fromthe received data. For instance, a processor may process informationassociated with the received sensor data to determine informationincluding a file type in which the data is stored, a type of the data, asensor that collected the data, and a timestamp for when the data wasreceived. Using the information returned from the processing, theprocessor may use the information to help determine a specific movementassociated with the device.

In one example, the plurality of sensors of the device may include anIMU and the data received at block 402 may include data sensed by theIMU. Accordingly, at block 404, when processing the data, the processormay determine the motion data, for example, by searching for ordetermining what data within the received data corresponds to the IMU.

For example, using the data received from the IMU a processor of thedevice may for the six degrees of freedom (e.g., x, y, z and θ_(x),θ_(y) and θ_(z)) integrate acceleration data received from the IMU unitover time to determine a velocity of the device. In another example, theprocessor may further integrate the velocity to determine a position. Asa specific example, based on processing the IMU data, the processor may,for example, detect that the device traveled westward for an hour at anaverage speed of 1.5 miles per hour, and then the processor maydetermine that the device is about 1.5 miles west of its initialposition or some starting, known, or reference position. The processormay determine an estimation of motion to be a path that the devicetraveled over the time period, for example. The estimation of motion maybe along any of the six degrees of freedom (e.g., x, y, z and θ_(x),θ_(y) and θ_(z)), or along a combination of any of the degrees offreedom. The estimation of motion may be in terms of changes inacceleration, velocity, position, or any combination of such factors.

In another example, the plurality of sensors of the device may include adepth sensor and the data received may include data sensed by the depthsensor. When processing the data, the processor may parse out the datareceived from the depth sensor and may estimate a motion and associateddepth of the motion. In yet further examples, the plurality of sensorsmay include a gyroscope or other, similar sensor configured to measureangular movement, and the received data may include data indicative ofangular motion. In similar form to the processing noted above, aprocessor of the device may process the received data to determine thedata indicative of the angular motion, and using that data, theprocessor of the device may estimate an angle of movement of the device.

Note the example motion data described above are not intended to belimiting, and other, similar motion data may be determined at block 404.The data may be indicative of any motion (e.g., an azimuth motion) ofthe device and may include any data sensed by any of the sensorsdescribed above with regard to FIGS. 1-3. Additionally, the determinedmotion data need not include only one type of motion data and may, attimes, include some or all of the data described above.

At block 406, method 400 includes as the device moves (or operates) inthe environment, receiving image data from a camera(s) of the device. Insome examples, the image data may be captured at the same time as thedata received from the plurality of sensors as described at block 402and may be included in the sensor data received at block 402. The imagedata may be received, for example, using a camera the same as or similarto cameras 208, 210, and 212 discussed with regard to FIG. 2. In oneexample, where the device includes a mobile phone having a rear facingcamera, the camera may capture images of environment 500 within a fieldof view of the device. In other examples where the device includes botha rear and a front facing camera, one or both cameras may be configuredto capture images of environment 500. Any amount of image data may becaptured, and in some examples, the camera may also be configured tocapture live video during the time window, and frames of the video maybe taken as images.

For example, the device may capture an image of environment 500, asshown in FIG. 5A, that may be included in the sensor data 502. The imagemay include image data indicative of the characteristics of environment500 including, for example, the house 504, the tree 516, the yard 506,the window 508, the door 510, the roof 512, and the chimney 514. In someexamples, the image data may also include a plurality of frames of imagedata that is indicative of the environment (e.g., shown as 520, 522, and524 in FIG. 5B). Taken together, the plurality of frames of image datamay be indicative of a time period during which the image data wasacquired. For instance, the plurality of frames of image data mayinclude image data that may be obtained at different times within 1-3seconds while the device is moving and operating within environment 500,and may include a combination of data collected by various sensors ofthe device (such as any data described as being collected by sensors ofthe device 100 in FIG. 1 or device 200 of FIG. 2, for example) and imagedata collected from a camera of the device. Alternatively, although notshown in FIG. 5B, the plurality of frames of image data may beindicative of a range/depth within which the sensor data was acquired.

Once the image data is captured, at block 408, method 400 includesselecting features in the image data for feature following. The featuresmay be selected based on the motion data determined at block 404.Generally, the image data can be processed based on the motion data toidentify a set of features, or points in the images that a featurefollowing (or tracking) algorithm can lock onto and follow throughmultiple frames of data. Example features points may be points in theimage that are unique, bright/dark spots, or edges/corners depending onthe particular tracking algorithm. For instance, an edge may include apoint in an image where there is a boundary between two image regions,and a corner may refer to point-like feature in an image that has alocal two-dimensional structure.

Determining the set of features for following may be performed invarious manners. In one example, a processor of the device may determinea direction of gravity (or gravity vectors) with respect to anorientation of the device based on the motion data. For instance, theprocessor may analyze or process data received from an IMU of the deviceand based on acceleration data received from the IMU determine thedirection of gravity. Using this information, vertical lines in theimage data may be determined as lines that are parallel to the directionof gravity and horizontal lines in the image data may be determined aslines that are perpendicular to the direction of gravity. Thereafter,the set of features may include the determined vertical lines orhorizontal lines as needed.

For instance, referring to FIG. 5B, a device may receive data indicativeof environment 500 from a plurality of sensors and the data may includeimage data 520, 522, and 524 of environment 500. Based on the datareceived, the device may determine that the direction of gravity is in asouthward, down direction (indicated by the arrow in FIG. 5B). Based onthis determination, the device may determine horizontal lines (e.g.,lines that are perpendicular to the direction of gravity) of data 520indicative of environment 500 such as lines 520A (bottom of the roof),520B (bottom of the window), and 520C (bottom of the house). Byfollowing these feature lines through the various frames of data, inwhich 522A, 522B, and 522C and 524A and 524C correspond to 520A, 520B,and 520C, in their respective frames, the device may use thisinformation to help estimate how the device moved, which will bedescribed in greater detail below. Alternatively, instead of horizontallines vertical lines may be determined for following or, in some cases,both horizontal and vertical lines may be moved for following.

In other examples, the set of features may be determined based on motiondata indicative of the device remaining still or, in other words, themovement of the device may include being held steady. In suchcircumstances, the set of features for following may be determined bypartitioning or dividing the image data and selecting features acrossthe partitions in a uniform manner. For instance, the image data may besplit into quadrants or sections and features may be selected from eachquitrent in a manner such that the each quadrant or section has the samenumber of features for following. Alternatively, features may beselected from each quadrant based on a threshold. For instance, featuresmay be selected in each quadrant until each quadrant includes a numberof features over a minimum threshold. In some examples, such as when adevice operates a wide-angle view camera, the image data may bepartitioned into sections based on angles instead of quadrants. In otherexamples, image data may be partitioned based on a depth associated withthe motion data. Regardless of the manner in which the image data ispartitioned, the features for following may be selected from eachpartition based on a threshold.

For example, referring to FIG. 5C, the image data 502 may be partitionedinto four quadrants A, B, C, and D. In attempt to ensure uniformity andthat enough features are obtained to help predict an accurate movementof the device, features may be selected from each quadrant of the imagedata. For instance, in the example shown in FIG. 5C, two features may beselected from each quadrant A (532A and 532B), B (532E and 532F), C(532C and 532D) and D (532G and 532H) of the image data 502 forfollowing. Accordingly, as the selected features move over variousframes of data (e.g., shown in FIG. 5B) each quadrant will have twofeatures that may be used to follow, and ultimately aid in determiningan accurate movement of the device.

In yet another example, the set of features may be determined based onan uncertainty associated with the movement of the device. Similar tothe other methods that may be used to determine the set of features, theuncertainty may be determined by processing the motion data estimated atblock 404. For instance, after processing the motion data, a processorof the device may determine that the device has executed a movement tochange a pitch or a roll of the device, but may be uncertain as to theexact measurements of that movement (e.g., the processor may not be ableto determine a degree or extent of the change in pitch or roll). Becausepitch and roll of the device may cause horizontal lines to move(seemingly move from the perspective of a field of view of the device)in the environment when the device is performing a pitch or a rollmaneuver, the set of features selected may correspond to horizontallines in the environment. Similarly, the set of features may bedetermined based on an uncertainty associated with a yaw movement of thedevice. Because yaw movements may cause vertical lines to move(seemingly move from the perspective of a field of view of the device)in the environment when the device is performing a yaw maneuver, the setof features selected may correspond to vertical lines in theenvironment.

For example, as shown in FIG. 5D, as device 300 moves in a manner thatchanges the yaw (shown by the curved arrow) of the device, verticallines may appear to be moving relative to the device. Accordingly,vertical lines such as line 530A, a left wall of the house, may beselected as a feature of interest for following. Then, upon followingvertical line 530A as the device moves in a western (or left) heading,for example, vertical line 520A of data 502 may move to a new position,shown as 530B in frame of data 502′ of FIG. 5D. Using the distance,between, the two vertical lines 530A and 530B, for example, a processorof the device may be able to estimate an extent and direction of the yawmotion.

In yet further examples, the set of features may be determined based onan uncertainty of the position of the device as it captures the data. Insuch an instance, the set of features may be determined based on pointsin the image data close to the device that have a certain threshold ofparallax. In other examples, the features may be determined based on theenvironment itself and shapes of the environment. For example, referringto the environment illustrated in FIG. 5A, knowing the house 504includes certain component such as a window 508 and door 510, aprocessor may determine that the door and the window to serve asfeatures for following and track several features (e.g., horizontallines, color, etc.) of components to ensure those features can beadequately followed. Additionally, in some environments, uncertaintyassociated with the environment may be used to determine features forfollowing. For example, referring to FIG. 5A, if a portion of theenvironment 500 is uncertain (e.g., an area behind tree 514) thenfeatures in that portion may be selected for following.

Note the examples described above are not intended to be limiting, andother, similar methods may be used to determine features for followingat block 408. The methods chosen may be any method that utilizes themotion data to help determine which features to follow.

Once the set of features for following have been determined, any featuretracking algorithm may be used to follow the features. For example, anedge detection technique may identify points in a digital image at whichimage brightness changes sharply or has discontinuities. The points atwhich image brightness changes sharply can be organized into a set ofcurved line segments termed edges. Edge detection may be applied todetect and follow the vertical and horizontal lines, for example. Cornerdetection is another approach used within computer vision systems toextract certain kinds of features and infer contents of an image, and acorner can be defined as an intersection of two edges. A corner can alsobe defined as a point for which there is two dominant and different edgedirections in a local neighborhood of the point. Other feature detectionmethods include, blob detection, which refers to methods that are aimedat detecting regions in a digital image that differ in properties, suchas brightness or color, compared to areas surrounding those regions. Thecorner and other detection methods may be used to, for example, detectparticular aspects of the image data such as the window 508 of data 502.

Algorithms such as the Harris & Stephens algorithm can be used to detectedges and corners. Still other methods may be used as well, such asfeature detection from the Accelerated Segment Test (FAST) algorithm todetect corners and interest points, for example. Furthermore, featuressuch as blobs may describe regions of interest in an image (e.g.,regions that are too smooth to be detected by a corner detector), andthe FAST algorithm, among others, can also be used to detect blobs aswell.

One example feature tracking method includes the Kanade-Lucas-Tomasi(KLT) feature tracker method. In one instance, features are located byexamining a minimum eigenvalue of each 2 by 2 gradient matrix of theimage, and features can be tracked using a Newton-Raphson method ofminimizing a difference between two windows of the images.

At block 410, the method 400 includes estimating one or more of aposition of the device or a velocity of the device in the environment assupported by the data from the plurality of sensors andfeature-following of the selected features in the images. For example,the processor can determine an estimation of motion of the device basedon the image data captured by the camera and the various featuresfollowed within the image data. In some examples, the processor mayimprove upon the motion data to make the estimation.

Once a feature is identified in consecutive images, an estimate of themotion of the device can be determined due to movement of the feature inthe images. For example, if the feature is of a static object, thenmovement of the feature within the images is due to movement of thedevice. In some examples, movement of features in the images betweenconsecutive images can be determined and associated as movement offeatures representing a moving object in the images. When the featuresrepresent a moving object, the movement of features between images maynot be due to movement of the device (or may be due to movement of theobject and movement of the device), and thus, the feature trackingmethods may not be accurate. As a result, features other than thoseassociated with the moving object can be used for the feature trackingWhen the features are associated with a static object, the features maybe used for the feature tracking

In another example, a pose or orientation of the camera of the devicefor the sliding time window can be determined based on the overallestimation of motion of the device. The IMU data may indicate yaw,pitch, and roll of the device, and the camera is in a fixed positionwith respect to the device. Thus, using the fixed relative position ofthe camera and the IMU data, an orientation of the camera can bedetermined. This may be helpful, for example, in instances in whichfeatures in the images may be representative of objects upside down orotherwise angled with respect to the camera, and the pose/orientationcan be used to translate the images to be upright for feature trackingpurposes.

FIG. 6 is a conceptual illustration of example estimations of motion ofthe device based on camera images and IMU data. As shown in FIG. 6, anexample camera estimation of motion of the device has been determinedusing feature tracking of features in images (such as tracking thehorizontal line features in consecutive images shown in FIG. 5B). Theestimation of motion is shown as a relative position along an x-axisover time. The graph further shows an estimation of motion of the devicebased on the IMU data, which varies at times from the camera estimationas shown by the difference. The difference may be tested at any timeover the time scale, or at multiple instances along the time scale, orat a beginning and ending of a sliding time window, for example, so asto determine if the difference exceeds the threshold. Exceeding thethreshold can be based on exceeding just once, exceeding at many times,etc., based on a desired accuracy of the estimations. In examples wherethe difference is larger than a threshold amount and exceeds a presetthreshold (both in terms of magnitude and instances of threshold beingexceeded), additional positional data can be determined.

It should be understood that arrangements described herein are forpurposes of example only. As such, those skilled in the art willappreciate that other arrangements and other elements (e.g. machines,interfaces, functions, orders, and groupings of functions, etc.) can beused instead, and some elements may be omitted altogether according tothe desired results. Further, many of the elements that are describedare functional entities that may be implemented as discrete ordistributed components or in conjunction with other components, in anysuitable combination and location, or other structural elementsdescribed as independent structures may be combined.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims, along with the full scope ofequivalents to which such claims are entitled. It is also to beunderstood that the terminology used herein is for the purpose ofdescribing particular embodiments only, and is not intended to belimiting.

What is claimed is:
 1. A method comprising: receiving, using aprocessor, data from a plurality of sensors of a device; determining,based on the data, motion data that is indicative of a movement of thedevice in an environment; as the device moves in the environment,receiving image data from a camera of the device; selecting, based atleast in part on the motion data, features in the image data forfeature-following, wherein selecting features in the image data forfeature-following comprises: determining, based on the image data, aplurality of sections of a first image of the image data; and selectingfor each of the plurality of sections a threshold number of features forfeature-following; and estimating one or more of a position of thedevice or a velocity of the device in the environment as supported bythe data from the plurality of sensors and by feature-following of theselected features in the image data.
 2. The method of claim 1, whereinthe plurality of sensors includes an inertial measurement unit (IMU),wherein the motion data includes data received from the IMU that isindicative of a direction of gravity being applied to the device, andwherein selecting features in the image data for feature-followingfurther comprises: identifying one or more vertical lines in the imagedata as a line that is parallel to the direction of gravity; andproviding the identified vertical line as a given feature forfeature-following.
 3. The method of claim 1, wherein the plurality ofsensors includes an inertial measurement unit (IMU), wherein the motiondata includes data received from the IMU that is indicative of adirection of gravity being applied to the device, and wherein selectingfeatures in the image data for feature-following further comprises:identifying one or more horizontal lines in the image data as a linethat is perpendicular to the direction of gravity; and providing theidentified horizontal line as a given feature for feature-following. 4.The method of claim 1, wherein the plurality of sensors includes a depthsensor, wherein the motion data includes data received from the depthsensor that is indicative of a plurality of depths associated with themovement of the device, and wherein selecting features in the image datafor feature-following further comprises: determining, based onrespective depths of the plurality of depths associated with themovement of the device, a plurality of cells in the image data that aredivided by respective depths of the plurality of depths; and selectingfor each of the plurality of cells a threshold number of features forfeature-following.
 5. The method of claim 1, wherein respective sectionsof the plurality of sections are defined based on quadrants associatedwith the image data.
 6. The method of claim 1, wherein the cameracomprises a wide-angle view camera, and wherein respective sections ofthe plurality of sections are defined based on angles associated withthe image data.
 7. The method of claim 1, further comprising: receiving,using the processor, environmental data from the plurality of sensors ofthe device, wherein the environmental data includes shape data thatdefines one or more shapes of objects in the environment; and whereinselecting features in the image data for feature following furthercomprises selecting features in the image data for feature-followingfurther based at least in part on the shape data.
 8. The method of claim1, further comprising: determining, based on the motion data, anuncertainty of movement of the device that defines a level ofuncertainty of a portion of the movement of the device; and whereinselecting features in the image data for feature-following comprisesselecting features in the image data for feature-following further basedon the uncertainty of movement of the device.
 9. The method of claim 8,wherein the uncertainty of movement of the device comprises anuncertainty of a roll or a pitch of the device, and wherein selectingthe features in the image data for feature-following comprises selectinghorizontal lines in the image data for feature-following.
 10. The methodof claim 8, wherein the uncertainty of movement of the device comprisesan uncertainty of a yaw of the device, and wherein selecting thefeatures in the image data for feature-following comprises selectingvertical lines in the image data for feature-following.
 11. Anon-transitory computer readable memory configured to store instructionsthat, when executed by a device, cause the device to perform functionscomprising: receiving, at the device, data from a plurality of sensorsof the device; determining, based on the data, motion data that isindicative of a movement of the device in an environment; as the devicemoves in the environment, receiving image data from a camera of thedevice; selecting, based at least in part on the motion data, featuresin the image data for feature-following, wherein selecting features inthe image data for feature-following comprises: determining, based onthe image data, a plurality of sections of a first image of the imagedata; and selecting for each of the plurality of sections a thresholdnumber of features for feature-following; and estimating one or more ofa position of the device or a velocity of the device in the environmentas supported by the data from the plurality of sensors and byfeature-following of the selected features in the image data.
 12. Thenon-transitory computer readable memory of claim 11, wherein theplurality of sensors includes an inertial measurement unit (IMU),wherein the motion data includes data received from the IMU that isindicative of a direction of gravity being applied to the device, andwherein selecting features in the image data for feature-followingfurther comprises: identifying one or more vertical lines in the imagedata as a line that is parallel to the direction of gravity or one ormore horizontal lines in the image data as a line that is perpendicularto the direction of gravity; and providing the identified vertical lineor the identified horizontal line as a given feature forfeature-following.
 13. The non-transitory computer readable memory ofclaim 11, wherein the plurality of sensors includes a depth sensor,wherein the motion data includes data received from the depth sensorthat is indicative of a plurality of depths associated with the movementof the device, and wherein selecting features in the image data forfeature-following further comprises: determining, based on respectivedepths of the plurality of depths associated with the movement of thedevice, a plurality of cells in the image data that are divided byrespective depths of the plurality of depths; and selecting for each ofthe plurality of cells a threshold number of features forfeature-following.
 14. The non-transitory computer readable memory ofclaim 11, further comprising: receiving environmental data from theplurality of sensors of the device, wherein the environmental dataincludes shape data that defines one or more shapes of objects in theenvironment; and wherein selecting features in the image data forfeature following further comprises selecting features in the image datafor feature following further based at least in part on the shape data.15. The non-transitory computer readable memory of claim 11, furthercomprising: determining, based on the motion data, an uncertainty ofmovement of the device that defines a level of uncertainty of a portionof the movement of the device; and selecting features in the image datafor feature-following further comprises selecting features in the imagedata for feature-following further based on the uncertainty of movementof the device.
 16. A device comprising: one or more processors; and datastorage configured to store instructions that, when executed by the oneor more processors, cause the device to perform functions comprising:receiving data from a plurality of sensors of the device; determining,based on the data, motion data that is indicative of a movement of thedevice in an environment; as the device moves in the environment,receiving image data from a camera of the device; selecting, based atleast in part on the motion data, features in the image data forfeature-following, wherein selecting features in the image data forfeature-following comprises: determining, based on the image data, aplurality of sections of a first image of the image data; and selectingfor each of the plurality of sections a threshold number of features forfeature-following; and estimating one or more of a position of thedevice or a velocity of the device in the environment as supported bythe data from the plurality of sensors and by feature-following of theselected features in the image data.
 17. The device of claim 16, whereinthe functions further comprise: receiving, using the one or moreprocessors, environmental data from the plurality of sensors of thedevice, wherein the environmental data includes shape data that definesone or more shapes of objects in the environment; and wherein selectingfeatures in the image data for feature-following further comprisesselecting features in the image data for feature-following further basedat least in part on the shape data.
 18. The device of claim 16, whereinthe functions further comprise: determining, based on the motion data,an uncertainty of movement of the device that defines a level ofuncertainty of a portion of the movement of the device; and whereinselecting features in the image data for feature-following furthercomprises selecting features in the image data for feature-followingfurther based at least in part on the uncertainty of movement of thedevice.
 19. The device of claim 16, wherein respective sections of theplurality of sections are defined based on quadrants associated with theimage data.
 20. The device of claim 16, wherein the camera comprises awide-angle view camera, and wherein respective sections of the pluralityof sections are defined based on angles associated with the image data.